Using an Optimal Multi-Target Image Segmentation Based Feature Extraction Method to Detect Hypervelocity Impact Damage for Spacecraft

Aerospace has become an indispensable part of exploring the universe. To guarantee the safety of the materials on aerospace, the recognition and the detection of hypervelocity impact (HVI) damage is needed. Processing bottlenecks and the lack of precision acquisition tools have restricted detection to the HVI damage of space debris for on-orbit spacecraft. This paper proposes an optimal multi-target image segmentation based feature extraction method to detect the HVI damage. For the collected infrared data, an improved mean shift clustering algorithm is given to adaptively classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstruction image reflecting the damage characteristics. Moreover, a multi-objective optimization segmentation model is proposed to segment the defects of infrared reconstructed images to further extract the geometric shape, depth and other features of various defects. To obtain accurate segmentation results of the infrared reconstructed thermal images, the segmentation objective functions are set with respect to noise elimination and detail reservation. A weight vectors adjustment algorithm is developed to further improve the accuracy of damage segmentation in the multi-objective optimization segmentation model, to derive better balance between detail retention and noise removal. The experimental results of the algorithm verify the effectiveness and benefits of the proposed method.